MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123862 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy Engineering College For Women; and Malla Reddy College Of Engineering And Technology, Medchal-Malkajgiri, Telangana, on Dec. 9, 2025, for 'dynamic workforce scheduling engine for gig economy platforms.'
Inventor(s) include Dr,mandala Sreenivas; Mrs. T. Pavani Prabha; Dr. K. Puspha Latha; Dr. K. Maddileti; and Mr. A. Mahesh.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "The present invention discloses a highly adaptive, algorithmic scheduling engine designed to optimize workforce allocation within the gig economy and on-demand service sectors. Traditional workforce management systems typically rely on static rosters or simple proximity-based dispatching, which often result in suboptimal utilization rates, increased idle time for workers, and inconsistent service delivery for customers. The disclosed invention overcomes these pervasive inefficiencies by implementing a multi-objective optimization framework that processes real-time variables-including worker location, skill proficiency, current fatigue levels, and traffic conditions-to dynamically pair service providers with service requests. The system comprises a central processing unit capable of ingesting high-velocity data streams from mobile devices used by the workforce. This data is fed into a machine-learning model that not only reacts to incoming service requests but also predicts demand hotspots based on historical patterns. By forecasting demand surges in specific geographic zones, the engine proactively guides workers toward high-probability areas before requests are even placed, thereby drastically reducing the "time-to-arrival" metric and balancing supply and demand equilibrium across a city-wide grid. Furthermore, the invention introduces a unique "suitability scoring" mechanism. Rather than simply assigning the nearest worker to a task, the engine calculates a composite score for every available worker within a feasible radius. This score weighs the worker's past performance ratings, their specific expertise relative to the task requirements, and their current route efficiency. This ensures that complex tasks are routed to highly rated, experienced workers, while simpler tasks can be distributed to newer entrants, facilitating a fair distribution of work and maintaining high quality assurance standards. The technology is deployed as a cloud-based Software as a Service (SaaS) platform, featuring an API gateway that integrates seamlessly with existing mobile applications used by delivery aggregators, home service providers, and ride-sharing fleets. The system supports dynamic re-routing, meaning that if a worker faces an unexpected delay or cancellation, the schedule is instantly recalculated, and tasks are shuffled in real-time to minimize disruption, ensuring a robust and resilient operational workflow."
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